Life in 2.5D: Animal Movement in the Trees

The complex, interconnected, and non-contiguous nature of canopy environments present unique cognitive, locomotor, and sensory challenges to their animal inhabitants. Animal movement through forest canopies is constrained; unlike most aquatic or aerial habitats, the three-dimensional space of a forest canopy is not fully realized or available to the animals within it. Determining how the unique constraints of arboreal habitats shape the ecology and evolution of canopy-dwelling animals is key to fully understanding forest ecosystems. With emerging technologies, there is now the opportunity to quantify and map tree connectivity, and to embed the fine-scale horizontal and vertical position of moving animals into these networks of branching pathways. Integrating detailed multi-dimensional habitat structure and animal movement data will enable us to see the world from the perspective of an arboreal animal. This synthesis will shed light on fundamental aspects of arboreal animals’ cognition and ecology, including how they navigate landscapes of risk and reward and weigh energetic trade-offs, as well as how their environment shapes their spatial cognition and their social dynamics

Interaction with Multiple Data Visualizations Through Natural Language Commands

Data exploration stands to benefit from environments that permit users to examine and juxtapose many views of data, particularly views that present diverse selections of data values and attributes. Large, high-resolution environments are capable of showing many related views of data, but efficiently creating and displaying visualizations in these environments presents significant challenges. In this dissertation, I will present my research on “multi-view data exploration interactions” that enable users to create sets of views with coherent data value and attribute variations, through multi-modal speech and mid-air pointing gestures in large display environments. This work enables users to rapidly and efficiently generate sets of views in support of multi-view data exploration tasks, organize these views in coherent collections, and operate on sets of views collectively, rather than individually, to efficiently reach large portions of the ’data and attribute space’. I will present three contributions: 1) an observational study of data exploration in a large display environment with speech and mid-air gestures, 2) ’Traverse’, an interaction technique for data exploration, based on this study, which uses natural language to create and pivot sets of views, and 3) ’Ditto’, a multi-modal speech and mid-air pointing gesture interactive environment, which utilizes the multi-view data exploration technique, in large display environments

Into the (virtual) jungle

Research in animal behavior is changing. Traditionally, human researchers directly observed animals in remote settings and noted their behaviors. Now, researchers can also use sensor technologies to track animal movement and capture rich data about a habitat. This "Big Data" approach presents tremendous opportunities, tracking more animals over longer time spans and larger areas. However, it also removes researchers from direct data collection, making it harder to keep humans "in the loop". Working with anthropologist Meg Crofoot (UC Davis) and computer scientist Tanya Berger-Wolf (UIC), we are exploring how virtual reality might help animal behavior researchers view, explore and step inside their data. We have recreated the Barro Colorado Island, a research habitat in Panama, in CAVE2, an immersive environment at the Electronic Visualization Laboratory at UIC, using data from lidar scans and aerial photos. This visualization will allow our collaborators to stand on the ground, leap through the trees or fly above the canopy to observe how tracked animals forage for food. In this picture, we are looking at GPS positions of one capuchin monkey, visualized as spheres colored on a blue-orange gradient by time of day, and observing how often he visits pink-hued fruit trees in the canopy

Bacterial Gene Neighborhood Investigation Environment: A Scalable Genome Visualization for Big Displays

In the past decade, genome sequencing costs have decreased faster than Moore's Law, resulting in a rapid increase in the volume of genomic data. This increased rate of sequencing is particularly evident in bacterial genomics, where thousands of complete bacterial genomes are available for comparative analysis. This data has the potential to address a variety of important questions, so there is a crucial need for visualizations that scale to accommodate comparisons between hundreds of genomes at once. In parallel, advances in display hardware over the past decade have enabled rapid growth and affordability in display resolution and the development of large, high-resolution display environments. While recent research suggests that these environments present benefits to visualization designers, more research is needed to understand the design requirements for particular domain problems in order to best take advantage of these benefits. In this paper we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a new comparative gene neighborhood visualization designed to address large volumes of bacterial genome sequences by taking advantage of the special qualities of large, high-resolution displays